Show simple item record

A Long Sequence Time-Series Forecasting Method for Early Warning of Long Landing Risks with QAR Flight Data

dc.contributor.authorZeyuan Zhou
dc.contributor.authorXiaolei Chong
dc.contributor.authorZhenglei Chen
dc.contributor.authorJicheng Zhou
dc.contributor.authorJichao Zhang
dc.contributor.authorPengshuo Guo
dc.contributor.otherCollege of Aeronautical Engineering, Air Force Engineering University, Xi’an 710038, China
dc.contributor.otherCollege of Aeronautical Engineering, Air Force Engineering University, Xi’an 710038, China
dc.contributor.otherCollege of Aeronautical Engineering, Air Force Engineering University, Xi’an 710038, China
dc.contributor.otherTianjin Airlines Co., Ltd., Tianjin 300300, China
dc.contributor.otherCollege of Aeronautical Engineering, Air Force Engineering University, Xi’an 710038, China
dc.contributor.otherCollege of Aeronautical Engineering, Air Force Engineering University, Xi’an 710038, China
dc.date.accessioned2025-08-27T14:00:05Z
dc.date.accessioned2025-10-08T08:35:37Z
dc.date.available2025-10-08T08:35:37Z
dc.date.issued01-08-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/36159
dc.description.abstractLong landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. In response, a comprehensive risk prediction framework for aircraft long landings, supported by Quick Access Recorder (QAR) data, was constructed. The framework includes a data analysis pipeline, a sequence prediction model, and performance evaluation metrics for accident warning efficiency. Specifically, approximately 3 million rows of real QAR data were collected, and reasonable landing intervals were extracted based on pilots’ correct landing sightlines, attention allocation, and actual visual scenarios at departure heights. Gradient Boosting Decision Trees (GBDT) were employed to develop a method for extracting landing interval feature data, based on monitored parameters and ranges of landing distance. Additionally, the GBDT-Informer long-sequence time series prediction model was developed to forecast landing distance, accompanied by the construction of effective metrics for evaluating prediction performance. The results indicate that the GBDT-Informer model effectively models the temporal dimensions of landing intervals, accurately predicting ground speed (GS), radio altitude (RALT), and landing distance sequences. Compared to other prediction models, the GBDT-Informer model consistently achieved the smallest RMSE, MAE, and MAPE values, demonstrating high prediction accuracy. This predictive framework allows for the analysis of the coupling relationships among multiple parameters in flight data and their interrelations with exceedance anomalies. The findings can be applied in actual flight landings to promptly assess whether landing distances exceed limits, providing quick references for flight crews during landing or go-around decisions, thereby enhancing operational safety margins during the landing phase.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccMotor vehicles. Aeronautics. Astronautics
dc.titleA Long Sequence Time-Series Forecasting Method for Early Warning of Long Landing Risks with QAR Flight Data
dc.typeArticle
dc.description.keywordsQAR
dc.description.keywordsdata extraction
dc.description.keywordsinformer
dc.description.keywordsGBDT
dc.description.keywordslong landing
dc.description.keywordsrisk warning
dc.description.doi10.3390/aerospace12080744
dc.title.journalAerospace
dc.identifier.e-issn2226-4310
dc.identifier.oaioai:doaj.org/journal:66b1a0e519e24ca0a92dc5ef9391ca71
dc.journal.infoVolume 12, Issue 8


This item appears in the following Collection(s)

Show simple item record